Masini, Cristian (Petroleum Development Oman) | Al Shuaili, Khalid Said (Petroleum Development Oman) | Kuzmichev, Dmitry (Leap Energy) | Mironenko, Yulia (Leap Energy) | Majidaie, Saeed (Formerly with Leap Energy) | Buoy, Rina (Formerly with Leap Energy) | Alessio, Laurent Didier (Leap Energy) | Malakhov, Denis (Target Oilfield Services) | Ryzhov, Sergey (Formerly with Target Oilfield Services) | Postuma, Willem (Target Oilfield Services)
Unlocking the potential of existing assets and efficient production optimisation can be a challenging task from resource and technical execution point of view when using traditional static and dynamic modelling workflows making decision-making process inefficient and less robust.
A set of modern techniques in data processing and artificial intelligence could change the pattern of decision-making process for oil and gas fields within next few years. This paper presents an innovative workflow based on predictive analytics methods and machine learning to establish a new approach for assets management and fields’ optimisation. Based on the merge between classical reservoir engineering and Locate-the-Remaining-Oil (LTRO) techniques combined with smart data science and innovative deep learning algorithms this workflow proves that turnaround time for subsurface assets evaluation and optimisation could shrink from many months into a few weeks.
In this paper we present the results of the study, conducted on the Z field located in the South of Oman, using an efficient ROCM (Remaining Oil Compliant Mapping) workflow within an advanced LTRO software package. The goal of the study was to perform an evaluation of quantified and risked remaining oil for infill drilling and establish a field redevelopment strategy.
The resource in place assessment is complemented with production forecast. A neural network engine coupled with ROCM allowed to test various infill scenarios using predictive analytics. Results of the study have been validated against 3D reservoir simulation, whereby a dynamic sector model was created and history matched.
Z asset has a number of challenges starting from the fact that for the last 25 years the field has been developed by horizontal producers. The geological challenges are related to the high degree of reservoir heterogeneity which, combined with high oil viscosity, leads to water fingering effects. These aspects are making dynamic modelling challenging and time consuming.
In this paper, we describe in details the workflow elements to determine risked remaining oil saturation distribution, along with the results of ROCM and a full-field forecast for infill development scenarios by using neural network predictive analytics validated against drilled infills performance.
A new sulfonate surfactant from non-edible vegetable oils is developed. The sulfonate surfactant was synthesized from C16-18 fatty acid and evaluated for stability, Interfacial Tension (IFT) and core flooding experiments for its capability to enhance oil recovery. The feedstock is composed mostly of unsaturated fatty acid derived from jatropha curcas oil which can be converted to fatty acid methyl ester. The methyl ester is epoxidized and hydrolyzed to hydroxyl groups, which subsequently were sulfonated to form a product named Methyl Ester Sulfonates (MES).
The performance of the synthesized surfactant was studied for its stability, IFT and core flooding experiments. The stability shows that surfactant solution in produced water is clear and free of precipitation. Based on IFT experiments optimum surfactant concentration is 1 wt% which resulted in a lowest possible IFT of 0.19 mN/m in the presence of Na2CO3 as alkali.
By conducting a displacement test, an improvement in oil recovery is observed. Surfactant floods perform to test how effectively a surfactant formulation can recover oil on tertiary oil recovery. The results confirmed that there is a possibility of developing new surfactants from vegetable oils. It can obviate the need for using petrochemicals substances in synthesizing surfactants.
Water alternating gas (WAG) injection has been a popular method for commercial gas injection projects worldwide. The injection of water and gas alternatively offers better mobility control of gas and hence, improves the volumetric sweep efficiency. Although the WAG process is conceptually sound, its field incremental recovery is disappointing as it rarely exceeds 5 to 10 % OOIP. Apart from operational problems, the WAG mechanism suffers from inherent challenges such as water blocking, gravity segregation, mobility control in high viscosity oil, decreased oil relative permeability, and decreased gas injectivity.
This paper addresses the aforementioned problems and proposes a new combination method, named as the chemically enhanced water alternating gas (CWAG), to improve the efficiency of WAG process. The unique feature of this new method is that it uses alkaline, surfactant, and polymer as a chemical slug which will be injected during WAG process to reduce the interfacial tension (IFT) and improve the mobility ratio. In a CWAG process, a chemical slug is chased by water, preceded by gas slug and followed by alternate CO2 and water slug or chemical slug injects after one cycle of gas and water slug. Essentially CWAG involves a combination of chemical flooding and immiscible carbon dioxide (CO2) injections. These mechanisms are IFT reduction, reducing water blocking effect, mobility control, oil viscosity reduction due to the CO2 dissolution and oil swelling.
CMG's STARS was used to study the performance of the new method using some of the data found in the literature. It is a chemical flood simulator that can simulate all aspects of chemical flooding, and it can also handle immiscible CO2 injection features by considering K-value partitioning. The sensitivity analysis shows that the new method gives a better recovery when compared to conventional WAG. This study shows the potential of CWAG to enhance oil recovery.
Upstream environmental policies require the use of best available and economically sound technology to continually reduce discharge of mud and cuttings waste resulting from drilling operations. Downhole disposal of mud and cuttings waste through hydraulic fracturing, Drill Cuttings Re-injection(DCRI), is often the preferred waste management option because it can achieve true zero discharge without being limited by the drilling location and it is also economically favorable.
Iran's petroleum industry produces large amount of drilling wastes annually. The giant oilfields, the wide drilling operations without any plan to treat or to dispose of wastes, cause so dangerous environmental problems that damage the agriculture and animal husbandry in these fields.
This paper tries to investigate the possibility of drill cuttings re-injection (DCRI) in Ahwaz oilfield. The volume of drilling waste is calculated for a typical well. Disposal formation selection is the next important and sensitive step. By using a hydraulic fracturing simulator, the lateral and vertical extent of the fractures can be estimated and modeled to ensure they do not cut across overlying sealing beds, intersect with other well bores, natural fractures, faults, drinking water aquifers.
A data gathering for subsurface geology and logging analysis and also the position of potable water sources and recoverable hydrocarbons throughout the field are shown that the Mishan formation is more appropriate for DCRI. The depth of almost 5000 ft of Mishan assures that created fractures are developed below enough underneath surface and far from hydrocarbon reservoir in 10000 ft depth. Numerous scenarios were considered in the feasibility studies to ensure that fractures do not breakthrough to surface during cuttings re-injection in the absence of stress or permeability barriers of upper formations. Even under the most severe conditions, it was found that the generated cuttings slurry could be safely injected.